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https://doi.org/10.5705/ss.2010.216
Title: | Extended BIC for small-n-large-P sparse GLM | Authors: | Chen, J. Chen, Z. |
Keywords: | Consistency Exponential family Extended Bayes information criterion Feature selection Generalized linear model Small-n-large-P |
Issue Date: | Apr-2012 | Citation: | Chen, J., Chen, Z. (2012-04). Extended BIC for small-n-large-P sparse GLM. Statistica Sinica 22 (2) : 555-574. ScholarBank@NUS Repository. https://doi.org/10.5705/ss.2010.216 | Abstract: | The small-n-large-P situation has become common in genetics research, medical studies, risk management, and other fields. Feature selection is crucial in these studies yet poses a serious challenge. The traditional criteria such as AIC, BIC, and cross-validation choose too many features. In this paper, we examine the variable selection problem under the generalized linear models. We study the approach where a prior takes specific account of the small-n-large-P situation. The criterion is shown to be variable selection consistent under generalized linear models. We also report simulation results and a data analysis to illustrate the effectiveness of EBIC for feature selection. | Source Title: | Statistica Sinica | URI: | http://scholarbank.nus.edu.sg/handle/10635/105148 | ISSN: | 10170405 | DOI: | 10.5705/ss.2010.216 |
Appears in Collections: | Staff Publications |
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